Authors:
Artur Ferreira
1
and
Mário Figueiredo
2
Affiliations:
1
Instituto Superior de Engenharia de Lisboa and Instituto de Telecomunicações, Portugal
;
2
Instituto Superior Técnico and Instituto de Telecomunicações, Portugal
Keyword(s):
Feature selection, Feature discretization, Microarray data, Tumor detection, Cancer detection.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
BioInformatics & Pattern Discovery
;
Clustering and Classification Methods
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining High-Dimensional Data
;
Soft Computing
;
Symbolic Systems
Abstract:
Tumor and cancer detection from microarray data are important bioinformatics problems. These problems are quite challenging for machine learning methods, since microarray datasets typically have a very large number of features and small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. This paper proposes unsupervised feature discretization and selection methods suited for microarray data. The experimental results reported, conducted on public domain microarray datasets, show that the proposed discretization and selection techniques yield competitive and promising results with the best previous approaches. Moreover, the proposed methods efficiently handle multi-class microarray data.